Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

Chapoy, Antonin; Mohammadi, Amir H.; Richon, Dominique

doi:10.2516/ogst:2007048

Cited by 69 publications

(207 citation statements)

References 14 publications

(25 reference statements)

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“…It consists of highly inter-connected elements that are known as neurons [34]. As a parallel, distributive and adaptive system, it can be effectively applied in complex and non-linear problems in different engineering disciplines [35][36][37].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

Karambeigi

Asl

Nasiri

2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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-Microemulsion flooding is one of the most effective methods of Chemical Enhanced Oil Recovery (CEOR), particularly for the production of residual oil trapped in unconventional reservoirs. A critical step for successful application of this technique is to achieve a suitable formulation. Previous studies have almost focused on the technical aspects while considering both practical and economic matters as conflicting objectives have been neglected. In the present paper, the formulation of microemulsion is optimized based on the trade-off between scientific and financial responses using a hybrid workflow in which experimental design and artificial intelligence methodologies are composed. To appraise the efficiency of developed algorithm, a challenge case study is first evaluated and compared to previous approaches. Thereafter, the second case is examined in which a newly developed formulation of microemulsion for high temperature carbonate reservoirs is optimized. The outcomes of this multi-attribute workflow are compared to a single-objective algorithm. The results indicate the outstanding performance of the proposed approach for multi-objective optimization of microemulsion formulation. Eventually, the possible concerns regarding the application of microemulsion flooding in unconventional reservoirs are discussed.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

Karambeigi

Asl

Nasiri

2018

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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“…Since the experimental data used in ANN had different ranges, all input and output experimental data were normalized to the range of 0-1. This data normalization was performed to prevent disorder in the learning process and gain homogenous results and higher ANN performance [32,33].…”

Section: Ann Model Descriptionmentioning

confidence: 99%

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Bashipour

Rahimi

Khorasani

et al. 2017

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-The existence of hydrogen sulfide (H 2 S) in the gas effluents of oil, gas and petrochemical industries causes environmental pollution and equipment corrosion. These gas streams, called offgas, have high H 2 S concentration, which can be used to produce sodium sulfide (Na 2 S) by H 2 S reactive absorption. Na 2 S has a wide variety of applications in chemical industries. In this study, the reactive absorption process was performed using a spray column. Response Surface Methodology (RSM) was applied to design and optimize experiments based on Central Composite Design (CCD). The individual and interactive effects of three independent operating conditions on the weight percent of the produced Na 2 S (Y) were investigated by RSM: initial NaOH concentration (10-20% w/w), scrubbing solution temperature (40-60°C) and liquid-to-gas volumetric ratio (15 9 10 À3 to 25 9 10 À3 ). Furthermore, an Artificial Neural Network (ANN) model was used to predict Y. The results from RSM and ANN models were compared with experimental data by the regression analysis method. The optimum operating conditions specified by RSM resulted in Y of 15.5% at initial NaOH concentration of 19.3% w/w, scrubbing solution temperature of 40°C and liquid-to-gas volumetric ratio of 24.6 9 10 À3 v/v.Résumé -Optimisation expérimentale et modélisation de la production de sulfure de sodium à partir d'H 2 S riche en gaz provenant de la surface d'intervention méthodologique et de réseau de neurones artificiels -La présence de sulfure d'hydrogène (H 2 S) dans les effluents gazeux rejetés par l'industrie pétrolière, gazière et pétrochimique entraîne une pollution de l'environnement ainsi que la corrosion des équipements. Ces flux gazeux, appelés effluents gazeux, présentent une forte concentration de H 2 S qui peut être employée pour produire du sulfure de sodium (Na 2 S) par absorption réactive de H 2 S. Le Na 2 S trouve une multitude d'applications dans les industries chimiques. Dans cette étude, le processus d'absorption réactive a été réalisé avec une colonne à pulvérisation. La méthode des surfaces de réponses (Response Surface Methodology, RSM) a été appliquée pour concevoir et optimiser des expériences basées sur les plans composites centrés (Central Composite Design, CCD). Les effets individuels et interactifs de trois conditions d'exploitation indépendantes sur le pourcentage en poids de Na 2 S produit (Y) ont été analysés par RSM, à savoir : la concentration de NaOH initiale (10 à 20 % p/p), la température de la solution de lavage (40 à 60°C) et le rapport du volume liquide/gaz (15 9 10

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“…The output variable is the pressure [Pa] of hydrate formation. It has to be mentioned that to achieve a better stability, the following scaling rule is applied to pressure before training (Chapoy et al, 2007): In order to avoid over fitting and be assured of generalization ability and predictability of the neural network, the training data set was divided into two subsets including training and test data sets. Two thirds of all experimental data were randomly selected and used for training and the rest were used for testing the network.…”

Section: Application Of Annmentioning

confidence: 99%

Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

Saber¹

2012

Int. J. Thermo

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The objective of this work is making comparison between thermodynamic models and data-driven techniques accuracy in prediction of hydrate formation pressure as a function of temperature and composition of gas mixtures. The Peng-Robinson (PR) and Patel-Teja (PT) equations of state are used for thermodynamic modeling and Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used as data-driven models. The capability of each method is evaluated by comparison with the experimental data collected from literature. It is shown that there is a good agreement between thermodynamic modeling and the experimental data in most of the cases; however, the prediction relative errors are more than 10% in some cases. The data-driven models are trained and tested using a set of experimental data and their optimum structures are selected based on the prediction error of the test data set. The accuracy of ANN for prediction of hydrate formation pressure is slightly better than those of PR and PT. The prediction errors of ANFIS for all cases are less than 1% which is very promising and proves the potential of ANFIS as a capable tool for prediction of the hydrate formation pressure.

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Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

Cited by 69 publications

References 14 publications

Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

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Predicting the Hydrate Stability Zones of Natural Gases Using Artificial Neural Networks

Cited by 69 publications

References 14 publications

Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

Multi-Objective Optimization of Microemulsion Flooding for Chemical Enhanced Oil Recovery

Experimental Optimization and Modeling of Sodium Sulfide Production from H2S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Comparative analysis of hydrate formation pressure applying cubic Equations of State (EoS), Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)

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About

Experimental Optimization and Modeling of Sodium Sulfide Production from H₂S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network